corrcounts_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/corrcounts_merge.rds")
metadata_merge <- readRDS("~/VersionControl/senescence_benchmarking/Data/metadata_merge.rds")
SenescenceSignatures <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_divided_newCellAge.RDS")
set.seed("123456")
metadata_merge_corr <- metadata_merge
metadata_merge_corr$random_cat <- sample(c("A","B","C"), nrow(metadata_merge_corr), replace = T)
metadata_merge_corr$random_numeric <- sample(0:100, nrow(metadata_merge_corr), replace = TRUE)
metadata_merge_corr$Is_Senescent <- ifelse(metadata_merge_corr$Condition == "Senescent", "Senescent", "Non Senescent")
library(markeR)
library(ggplot2)
library(ggpubr)
library(edgeR)
?markeR
?CalculateScores
ℹ Rendering development documentation for "CalculateScores"
df_ssGSEA <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = SenescenceSignatures)
Considering unidirectional gene signature mode for signature [DOWN]_CellAge
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
df_logmedian <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = SenescenceSignatures)
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_logmedian, ColorVariable = "CellType", GroupingVariable="Condition", method ="logmedian", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = SenescenceSignatures)
Considering unidirectional gene signature mode for signature [DOWN]_CellAge
Considering unidirectional gene signature mode for signature [DOWN]_HernandezSegura
Considering unidirectional gene signature mode for signature [DOWN]_SeneQuest
Considering unidirectional gene signature mode for signature [UP]_CellAge
Considering unidirectional gene signature mode for signature [UP]_HernandezSegura
Considering unidirectional gene signature mode for signature [UP]_SeneQuest
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition", method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 6, nrow = 2, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
plotlist <- list()
for (sig in names(df_ssGSEA)){
df_subset_ssGSEA <- df_ssGSEA[[sig]]
df_subset_logmedian <- df_logmedian[[sig]]
df_subset_merge <- merge(df_subset_ssGSEA,df_subset_logmedian,by="sample")
# Wrap the signature name using the helper function
wrapped_title <- wrap_title_aux(sig, width = 20)
plotlist[[sig]] <- ggplot2::ggplot(df_subset_merge, aes(x=score.x, y=score.y)) +
geom_point(size=4, alpha=0.8, fill="darkgrey", shape=21) +
theme_bw() +
xlab("ssGSEA Enrichment Score") +
ylab("Normalised Signature Score") +
ggtitle(wrapped_title) +
theme(plot.title = ggplot2::element_text(hjust = 0.5, size=10),
plot.subtitle = ggplot2::element_text(hjust = 0.5))
}
ggpubr::ggarrange(plotlist=plotlist, nrow=3, ncol=4, align = "h")
Try scores with bidirectional signatures
bidirectsigs <- readRDS("~/VersionControl/senescence_benchmarking/CommonFiles/SenescenceSignatures_complete_newCellAge.RDS")
for (sig in names(bidirectsigs)){
sigdf <- bidirectsigs[[sig]]
sigdf <- sigdf[,1:2] # remove the third column, if applicable
if(any(sigdf[,2]=="not_reported")){
sigdf <- sigdf[,1]
bidirectsigs[[sig]] <- sigdf
next
}
sigdf[,2] <- ifelse(sigdf[,2]=="enriched",1,-1)
bidirectsigs[[sig]] <- sigdf
}
bidirectsigs
$CellAge
$CSgene
[1] "TP53" "TERF2" "MAPK14" "CDKN2A" "CDKN1A" "CCNE1" "CCNA1" "MAPKAPK5" "CBX4" "TXN"
[11] "TBX2" "STAT3" "SRF" "BMI1" "MAP2K4" "MAP2K6" "MAP2K3" "MAPK8" "MAPK3" "MAPK1"
[21] "PRKCD" "PML" "OPA1" "ATM" "MDM2" "CXCL8" "IL6" "IGFBP7" "ID1" "HRAS"
[31] "H2AFX" "POT1" "SIRT1" "KDM6B" "PLA2R1" "EZH2" "E2F3" "E2F1" "CEBPB" "CDKN2D"
[41] "CDKN2B" "CDKN1B" "CDK6" "CDK4" "CDK2" "CDC42" "RBX1" "CDC27" "CDK1" "MAML1"
[51] "CD44" "MAD2L1BP" "MAP4K4" "AIM2" "RECQL4" "ARHGAP18" "KL" "MAPKAPK2" "AURKB" "SLC16A7"
[61] "CCNE2" "HIST1H2BJ" "HIST1H3F" "CCNA2" "MCM3AP" "CDC16" "TSC22D1" "CBS" "TNFSF13" "CTNNAL1"
[71] "EED" "PNPT1" "CDC23" "RNASET2" "TP63" "CAV1" "MKNK1" "TSLP" "HIST1H2BK" "PPM1D"
[81] "HAVCR2" "CBX2" "KDM2B" "DPY30" "C2orf40" "YPEL3" "HIST2H4A" "HIST1H4L" "HIST1H4E" "HIST1H4B"
[91] "HIST1H4H" "HIST1H4C" "HIST1H4J" "HIST1H4K" "HIST1H4F" "HIST1H4D" "HIST1H4A" "HIST1H3B" "HIST1H3H" "HIST1H3J"
[101] "HIST1H3G" "HIST1H3I" "HIST1H3E" "HIST1H3C" "HIST1H3D" "HIST1H3A" "HIST2H2BE" "HIST1H2BO" "HIST1H2BC" "HIST1H2BI"
[111] "HIST1H2BH" "HIST1H2BE" "HIST1H2BF" "HIST1H2BM" "HIST1H2BN" "HIST1H2BL" "HIST1H2BG" "HIST2H2AC" "HIST2H2AA3" "HIST1H2AB"
[121] "HIST1H2AC" "HIST1H2AJ" "HIST1H4I" "HIST3H3" "CALR" "HMGA2" "PHC3" "KAT6A" "EHMT1" "SMC6"
[131] "AIMP2" "CALCA" "DEK" "MAPKAPK3" "ZNF148" "YY1" "WRN" "WNT5A" "NR1H2" "UBE3A"
[141] "UBE2E1" "UBE2D1" "UBC" "UBB" "UBA52" "CDC26P1" "TYMS" "TWIST1" "HIRA" "RPS27AP11"
[151] "HIST2H2AA4" "TP73" "TOPÂ 1,00" "TNF" "TGFB2" "TGFB1" "TFDP1" "TERT" "TERF1" "BUB1B"
[161] "BUB1" "TCF3" "TBX3" "TAGLN" "STAT6" "STAT1" "BRAF" "SREBF1" "BRCA1" "SP1"
[171] "SOX5" "SOD2" "SNAI1" "SMARCB1" "SMARCA2" "HIST2H3D" "PHC1P1" "ACD" "SKIL" "LOC649620"
[181] "SLC13A3" "LOC647654" "SMURF2" "ANAPC1" "SHC1" "CPEB1" "H3F3AP6" "ZMAT3" "RBBP4P1" "SRSF3"
[191] "SRSF1" "SATB1" "S100A6" "RXRB" "RRM2" "RRM1" "RPS27A" "RPS6KA3" "RPS6KA2" "RPS6KA1"
[201] "RPL5" "RNF2" "RIT1" "RING1" "BCL2L1" "RELA" "BCL2" "CCND1" "RBP2" "RBL2"
[211] "RBL1" "RBBP7" "RBBP4" "NTN4" "RB1" "IL21" "RAN" "RAF1" "RAC1" "TNRC6C"
[221] "KIAA1524" "EP400" "CNOT6" "CBX8" "PTEN" "SEPN1" "BACH1" "PSMB5" "PROX1" "PRL"
[231] "MAP2K7" "MAP2K1" "MAPK10" "MAPK9" "MAPK11" "MAPK7" "PRKDC" "RNF114" "PRKCI" "ATF7IP"
[241] "MFN1" "PRKAA2" "CDKN2AIP" "RBM38" "PRG2" "HIST2H4B" "HJURP" "TMEM140" "PBRM1" "Mar-05"
[251] "PPARG" "PPARD" "POU2F1" "TERF2IP" "ERRFI1" "H2BFS" "PLK1" "PLAUR" "PIN1" "PIM1"
[261] "PIK3CA" "PHB" "PGR" "PGD" "PIAS4" "PDGFB" "SIRT6" "ANAPC11" "ANAPC7" "ANAPC5"
[271] "WNT16" "FZR1" "ZBTB7A" "ERGIC2" "PCNA" "FIS1" "PAX3" "NOX4" "MINK1" "PEBP1"
[281] "YBX1" "NINJ1" "NFKB1" "H2AFB1" "NDN" "NCAM1" "NBN" "MYC" "MYBL2" "MSN"
[291] "ASS1" "LOC441488" "MRE11A" "MOV10" "MMP7" "MIF" "MAP3K5" "MAP3K1" "MECP2" "MCL1"
[301] "MAGEA2" "SMAD9" "SMAD7" "SMAD6" "SMAD5" "SMAD4" "SMAD3" "SMAD2" "SMAD1" "MAD2L1"
[311] "MXD1" "MIR34A" "MIR30A" "MIR299" "MIR29A" "MIR22" "MIR217" "MIR21" "MIR205" "MIR203A"
[321] "MIR191" "MIR146A" "MIR141" "MIR10B" "ARNTL" "LMNB1" "LMNA" "LGALS9" "RHOA" "KRT5"
[331] "KRAS" "KIT" "KIR2DL4" "KCNJ12" "JUN" "JAK2" "ITGB4" "IRS1" "IRF7" "IRF5"
[341] "IRF3" "ING1" "IDO1" "ILF3" "IL15" "IL12B" "CXCR2" "IL4" "IGFBP5" "IGFBP3"
[351] "IGFBP1" "IGF1R" "IGF1" "H3F3AP5" "IFNG" "IFI16" "IDH1" "ID2" "HIST2H3A" "BIRC5"
[361] "HSPB1" "HSPA9" "HSPA5" "HSPA1A" "APEX1" "HNRNPA1" "FOXA3" "FOXA2" "FOXA1" "HMGA1"
[371] "HIF1A" "ANXA5" "HELLS" "HDAC1" "H3F3B" "H3F3A" "HIST1H2BB" "HIST1H2BD" "H2AFZ" "HIST1H2AD"
[381] "HIST1H2AE" "ANAPC4" "ANAPC2" "UBN1" "SENP1" "GUCY2C" "GSK3B" "UHRF1" "BRD7" "NSMCE2"
[391] "PTRF" "GPI" "GNAO1" "RPS6KA6" "TNRC6A" "AGO2" "B3GAT1" "DNAJC2" "GJA1" "AGO1"
[401] "EHF" "TINF2" "LDLRAP1" "ULK3" "GAPDH" "ABI3BP" "ASF1A" "HIST1H2BA" "G6PD" "ACKR1"
[411] "MTOR" "CDC26" "CNOT6L" "FOS" "CABIN1" "MORC3" "SUZ12" "NPTXR" "CBX6" "SIRT3"
[421] "CRTC1" "PPP1R13B" "SUN1" "SMC5" "TNRC6B" "FOXO1" "FOXM1" "TNIK" "SCMH1" "DKKÂ 1,00"
[431] "FGFR2" "FGF2" "HEPACAM" "FANCD2" "EWSR1" "ETS2" "ETS1" "ESR2" "ERF" "AKT1"
[441] "EREG" "ERBB2" "ENG" "ELN" "CRTC2" "EIF5A" "EGR1" "EGFR" "EEF1B2" "AGO4"
[451] "AGO3" "EEF1A1" "PHC2" "PHC1" "ABCA1" "E2F2" "DUSP6" "DUSP4" "HBEGF" "AGT"
[461] "DNMT3A" "AGER" "DKC1" "DAXX" "CYP3A4" "CTSZ" "CTSD" "CSNK2A1" "E2F7" "PARP1"
[471] "HIST3H2BB" "HIST2H3C" "JDP2" "HIST4H4" "CLU" "CKB" "RASSF1" "CHEK1" "TOPBP1" "UBE2C"
[481] "KIF2C" "BTG3" "EHMT2" "GADD45G" "NEK6" "ZMYND11" "SPINT2" "CENPA" "AGR2" "CEBPG"
[491] "HYOU1" "TADA3" "MCRS1" "NDRG1" "ANAPC10" "CDKN2C" "ZMPSTE24" "PSMD14" "NAMPT" "RAD50"
[501] "TRIM10" "DNM1L" "BCL2L11"
$GOBP_CELLULAR_SENESCENCE
[1] "AKT3" "MIR543" "CDK2" "CDK6" "CDKN1A" "ZMPSTE24" "CDKN1B" "CDKN2A" "CDKN2B" "CITED2" "KAT5" "PLK2"
[13] "NEK6" "ZNF277" "CGAS" "COMP" "MAPK14" "VASH1" "PLA2R1" "SMC5" "SIRT1" "MORC3" "NUP62" "ABL1"
[25] "ULK3" "RSL1D1" "FBXO5" "FBXO4" "MAGEA2B" "NSMCE2" "H2AX" "HLA-G" "HMGA1" "HRAS" "ID2" "IGF1R"
[37] "ING2" "KIR2DL4" "ARG2" "LMNA" "BMAL1" "MIR10A" "MIR146A" "MIR17" "MIR188" "MIR217" "MIR22" "MIR34A"
[49] "MAGEA2" "MAP3K3" "MAP3K5" "MIF" "MNT" "ATM" "NPM1" "YBX1" "OPA1" "PAWR" "ABI3" "FZR1"
[61] "WNT16" "SIRT6" "PML" "PRMT6" "PRELP" "PRKCD" "MAPK8" "MAPK11" "MAPK9" "MAPK10" "MAP2K1" "MAP2K3"
[73] "MAP2K6" "MAP2K7" "B2M" "ZMIZ1" "PTEN" "MIR20B" "RBL1" "BCL6" "MAP2K4" "BMPR1A" "SPI1" "SRF"
[85] "BRCA2" "NEK4" "TBX2" "TBX3" "MIR590" "TERC" "TERF2" "TERT" "TOP2B" "TP53" "TWIST1" "WNT1"
[97] "WRN" "SMC6" "KAT6A" "ZKSCAN3" "HMGA2" "CALR" "YPEL3" "ECRG4" "MAPKAPK5" "TP63" "PNPT1" "DNAJA3"
[109] "EEF1E1" "NUAK1"
$GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
$GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
$HernandezSegura
$REACTOME_CELLULAR_SENESCENCE
[1] "CDC27" "E2F2" "SCMH1" "MRE11" "MAP2K3" "MAPK9" "ANAPC4" "MAP2K4" "MAP4K4" "RPS6KA2" "UBE2D1" "EED"
[13] "MAP2K7" "TNRC6C" "MAPKAPK5" "ANAPC5" "TNRC6A" "TINF2" "AGO1" "CDC23" "CABIN1" "MAPK1" "HIRA" "TNRC6B"
[25] "E2F1" "RBBP7" "MAPK3" "ACD" "NBN" "CCNE1" "FZR1" "ERF" "CDK6" "H2AZ2" "EZH2" "MAPK8"
[37] "UBE2S" "MAP2K6" "NFKB1" "MAPK10" "ANAPC15" "CDKN1B" "PHC1" "ASF1A" "MAPK14" "E2F3" "LMNB1" "RAD50"
[49] "TFDP2" "MAPKAPK3" "IL1A" "RPS6KA1" "UBN1" "RNF2" "CDKN2C" "CDK2" "H1-3" "H1-1" "H2BC11" "CDKN1A"
[61] "ID1" "AGO3" "POT1" "CDKN2D" "CDC16" "H3-3B" "KDM6B" "TERF2" "CCNA1" "PHC2" "AGO4" "ETS1"
[73] "CDK4" "MDM2" "IL6" "TXN" "HMGA1" "RB1" "MINK1" "TP53" "ANAPC11" "CBX8" "CBX4" "RPS27A"
[85] "CCNA2" "H2BC1" "TERF1" "CDKN2B" "CDKN2A" "ATM" "HMGA2" "UBC" "VENTX" "ANAPC1" "TNIK" "MOV10"
[97] "ETS2" "H2BC5" "H4C8" "RBBP4" "MAPKAPK2" "H3-3A" "IGFBP7" "ANAPC10" "ANAPC16" "MAPK7" "TERF2IP" "H3-4"
[109] "BMI1" "H1-4" "STAT3" "CXCL8" "UBE2E1" "UBB" "FOS" "IFNB1" "CEBPB" "KAT5" "RELA" "PHC3"
[121] "CBX2" "UBE2C" "CCNE2" "ANAPC2" "CDC26" "RPS6KA3" "JUN" "SUZ12" "H2AC6" "H2BC4" "EHMT1" "EP400"
[133] "H3C13" "CBX6" "H2AC20" "H1-5" "H2BC21" "H2BC13" "MAPK11" "SP1" "H1-2" "H2AX" "H1-0" "ANAPC7"
[145] "H2AC7" "H2BC26" "H4C3" "H3C12" "H4C11" "H3C4" "MAP3K5" "H4C16" "H2BC12" "TFDP1" "MDM4" "H3C14"
[157] "H3C15" "RING1" "EHMT2" "UBA52" "H2AJ" "H4C15" "H4C14" "H4C12" "H2BC14" "H2BC8" "H3C8" "H2AB1"
[169] "H2BC6" "H4C6" "H2BC17" "H3C6" "H4C13" "H3C11" "H2BC9" "H3C1" "H4C9" "H2AC14" "H2BC3" "H4C5"
[181] "H2AC8" "H4C4" "H2BC7" "H3C7" "H2AC4" "H2BC10" "H4C1" "H4C2" "H3C10" "MIR24-2" "MIR24-1" "H3C2"
[193] "H3C3" "H2AC18" "H2AC19"
$SAUL_SEN_MAYO
$SeneQuest
NA
PlotScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = bidirectsigs, ColorVariable = "CellType", GroupingVariable="Condition", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend, compute_cohen=T)
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
PlotScores(data = corrcounts_merge, metadata = metadata_merge, method = "logmedian", gene_sets = bidirectsigs, ColorVariable = "CellType", GroupingVariable="Condition", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = NULL, compute_cohen=T, pvalcalc = F)
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
df_ssgsea <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ssGSEA", gene_sets = bidirectsigs)
Considering bidirectional gene signature mode for signature CellAge
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.
No id variables; using all as measure variables
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ssgsea, ColorVariable = "CellType", GroupingVariable="Condition", method ="ssGSEA", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
df_ranking <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge, method = "ranking", gene_sets = bidirectsigs)
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
cond_cohend <- list(A=c("Senescent"), # if no variable is defined, will be the first that appears in the ggplot
B=c("Proliferative","Quiescent"))
PlotScores(ResultsList = df_ranking, ColorVariable = "CellType", GroupingVariable="Condition", method ="ranking", ColorValues = cellTypes_colors, ConnectGroups=TRUE, ncol = 3, nrow = 3, widthTitle=20, y_limits = NULL, legend_nrow = 2,xlab=NULL, cond_cohend = cond_cohend)
Scores_Overview_All <- PlotScores(data = corrcounts_merge,
metadata = metadata_merge,
gene_sets=bidirectsigs,
GroupingVariable="Condition",
method ="all",
ncol = NULL,
nrow = NULL,
widthTitle=30,
limits = NULL,
title=NULL,
titlesize = 12,
ColorValues = NULL,
mode = "extensive",
widthlegend=22,
sig_threshold=0.05,
cohend_threshold=0.6,
PointSize=4,
colorPalette="Set3")
Considering bidirectional gene signature mode for signature CellAge
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Scores_Overview_All$heatmap
Scores_Overview_All$volcano
PlotScores(data = corrcounts_merge,
metadata = metadata_merge,
gene_sets=bidirectsigs,
GroupingVariable="Condition",
method ="all",
ncol = NULL,
nrow = NULL,
widthTitle=30,
limits = NULL,
title="Marthandan et al. 2016",
titlesize = 12,
ColorValues = NULL,
mode = "simple")
Considering bidirectional gene signature mode for signature CellAge
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
NA
NA
plt2 <- ROC_Scores(data = corrcounts_merge,
metadata = metadata_merge_corr,
gene_sets=bidirectsigs,
method = "logmedian",
variable ="Condition",
colors = "pink",
grid = TRUE,
spacing_annotation=0.35,
ncol=NULL,
nrow=NULL,
mode = "simple",
widthTitle = 28)
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
plt2 <- ROC_Scores(data = corrcounts_merge,
metadata = metadata_merge_corr,
gene_sets=bidirectsigs,
method = "logmedian",
variable ="Condition",
colors = "pink",
grid = TRUE,
spacing_annotation=0.35,
ncol=NULL,
nrow=NULL,
mode = "simple",
widthTitle = 28)
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
plt2
plt2 <- ROC_Scores(data = corrcounts_merge,
metadata = metadata_merge_corr,
gene_sets=bidirectsigs,
method = "all",
variable ="Condition",
colors = c(logmedian = "#3E5587", ssGSEA = "#B65285", ranking = "#B68C52"),
grid = TRUE,
spacing_annotation=0.35,
ncol=NULL,
nrow=NULL,
mode = "simple",
widthTitle = 28)
Considering bidirectional gene signature mode for signature CellAge
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
plt2
NA
If the user is investigating if a certain variable can be described from the score, and not already knowing that variable. More exploratory…
plt <- AUC_Scores(data = corrcounts_merge, metadata=metadata_merge_corr, gene_sets=bidirectsigs, method = "all", variable="Condition", nrow = NULL, ncol = NULL, limits = NULL, widthTitle = 28, titlesize = 12, ColorValues = c("#F9F4AE", "#B44141"), title = NULL)
Considering bidirectional gene signature mode for signature CellAge
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature CSgene
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature HernandezSegura
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
No id variables; using all as measure variables
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature SeneQuest
No id variables; using all as measure variables
No id variables; using all as measure variables
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
Considering bidirectional gene signature mode for signature CellAge
Considering unidirectional gene signature mode for signature CSgene
Considering unidirectional gene signature mode for signature GOBP_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_NEGATIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature GOBP_POSITIVE_REGULATION_OF_CELLULAR_SENESCENCE
Considering bidirectional gene signature mode for signature HernandezSegura
Considering unidirectional gene signature mode for signature REACTOME_CELLULAR_SENESCENCE
Considering unidirectional gene signature mode for signature SAUL_SEN_MAYO
Considering bidirectional gene signature mode for signature SeneQuest
plt
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```r
VariableAssociation(metadata_corr, target_var="score", cols = c("Condition","Is_Senescent","random_cat","random_numeric"),
discrete_colors = list(Is_Senescent=c("Senescent"="pink",
"Non Senescent"="orange")),
continuous_color = "#8C6D03",
color_palette = "Set2", nrow=1, sizeannot=3, legend.position="top")
`geom_smooth()` using formula = 'y ~ x'
Score_VariableAssociation(data=corrcounts_merge, metadata=metadata_merge_corr, cols = c("Condition","random_cat","random_numeric"), method="ranking", gene_set = list(HernandezSegura=bidirectsigs$HernandezSegura), mode = "extensive", nonsignif_color = "white", signif_color = "red", saturation_value=NULL,sig_threshold = 0.05, widthlabels=29, labsize=10, titlesize=14, pointSize=5)
Considering bidirectional gene signature mode for signature HernandezSegura
Warning in min(df_results_overall$padj) :
no non-missing arguments to min; returning Inf
`geom_smooth()` using formula = 'y ~ x'
options(error=recover)
Score_VariableAssociation(data=corrcounts_merge, metadata=metadata_merge_corr, cols = c( "random_numeric"), method="ranking", gene_set = list(HernandezSegura=bidirectsigs$HernandezSegura), mode = "extensive", nonsignif_color = "white", signif_color = "red", saturation_value=NULL,sig_threshold = 0.05, widthlabels=29, labsize=10, titlesize=14, pointSize=5)
Considering bidirectional gene signature mode for signature HernandezSegura
Warning in min(df_results_overall$padj) :
no non-missing arguments to min; returning Inf
`geom_smooth()` using formula = 'y ~ x'
senescence_triggers_colors <- c(
"none" = "#E57373", # Soft red
"Radiation" = "#BDBDBD", # Medium gray
"DNA damage" = "#64B5F6", # Brighter blue
"Telomere shortening" = "#4FC3F7", # Vivid sky blue
"DNA demethylation" = "#BA68C8", # Rich lavender
"Oxidative stress" = "#FDD835", # Strong yellow
"Conditioned Medium" = "#F2994A", # Warm orange
"Oncogene" = "#81C784", # Medium green
"Lipid Accumulation" = "#E57373", # Coral
"Calcium influx" = "#26A69A", # Deep teal
"Plasma membrane dysruption" = "#D32F2F", # Strong salmon
"OSKM factors" = "#FFB74D", # Bright peach
"YAP KO" = "#9575CD" # Deep pastel purple
)
IndividualGenes_Violins(data = corrcounts_merge, metadata = metadata_merge, genes = c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"), GroupingVariable = "Condition", plot=T, ncol=NULL, nrow=2, divide="CellType", invert_divide=FALSE,ColorValues=senescence_triggers_colors, pointSize=2, ColorVariable="SenescentType", title="Senescence", widthTitle=16,y_limits = NULL,legend_nrow=4, xlab="Condition",colorlab="")
options(error=recover)
CorrelationHeatmap(data=corrcounts_merge,
metadata = metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
separate.by = "Condition",
method = "pearson",
colorlist = list(low = "#3F4193", mid = "#F9F4AE", high = "#B44141"),
limits_colorscale = c(-1,0,1),
widthTitle = 16,
title = "test",
cluster_rows = TRUE,
cluster_columns = TRUE,
detailedresults = FALSE,
legend_position="right",
titlesize=20)
options(error=recover)
annotation_colors <- list(
CellType = c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
),
Condition = c(
"Senescent" = "#65AC7C", # Example color: greenish
"Proliferative" = "#5F90D4", # Example color: blueish
"Quiescent" = "#EDA03E" # Example color: orange
)
)
ExpressionHeatmap(data=corrcounts_merge,
metadata = metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
annotate.by = c("CellType","Condition"),
annotation_colors = annotation_colors,
colorlist = list(low = "#3F4193", mid = "#F9F4AE", high = "#B44141"),
cluster_rows = TRUE,
cluster_columns = FALSE,
title = "test",
titlesize = 20,
legend_position = "right",
scale_position="right")
cellTypes_colors <- c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
ROCandAUCplot(corrcounts_merge,
metadata_merge,
condition_var = "Condition",
class = "Senescent",
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
group_var="CellType",
plot_type = "all",
heatmap_params = list(col = list( "#F9F4AE" ,"#B44141"),
limits = c(0.5,1),
cluster_rows=T),
roc_params = list(nrow=2,
ncol=2,
colors=cellTypes_colors),
commomplot_params = list(widths=c(0.5,0.5)))
CohenDHeatmap(corrcounts_merge,
metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
condition_var = "Condition",
class = "Senescent",
group_var = "CellType",
title = NULL,
widthTitle = 16,
heatmap_params = list(col = list( "#F9F4AE" ,"#B44141"),
limits = NULL,
cluster_rows=T))
CellTypecols = c(
"Fibroblast" = "#FF6961", # Strong Pastel Red
"Keratinocyte" = "#FFB347", # Strong Pastel Orange
"Melanocyte" = "#FFD700", # Strong Pastel Yellow
"Endothelial" = "#77DD77", # Strong Pastel Green
"Neuronal" = "#779ECB", # Strong Pastel Blue
"Mesenchymal" = "#C27BA0" # Strong Pastel Purple
)
sencols <- c(
"Senescent" = "#D32F2F", # Strong salmon
"Quiescent" = "#FFB74D", # Bright peach
"Proliferative" = "#9575CD" # Deep pastel purple
)
plotPCA(data=corrcounts_merge,
metadata=metadata_merge,
genes=c("CDKN1A", "CDKN2A", "GLB1","TP53","CCL2"),
scale=FALSE,
center=TRUE,
PCs=list(c(1,2), c(2,3), c(3,4)),
ColorVariable="Condition",
ColorValues=sencols,
pointSize=5,
legend_nrow=1,
ncol=3,
nrow=NULL)
options(error=recover)
degenes <- calculateDE(data=corrcounts_merge,
metadata=metadata_merge,
variables="Condition",
lmexpression = NULL,
modelmat = NULL,
contrasts = c("Senescent - Proliferative",
"Senescent - Quiescent",
"Proliferative - Quiescent"))
degenes
$`Senescent-Proliferative`
$`Senescent-Quiescent`
$`Proliferative-Quiescent`
NA
options(error=recover)
plotVolcano(DEResultsList=degenes, genes=bidirectsigs, N=NULL, x="logFC",y="-log10(adj.P.Val)", pointSize=2, color="pink", highlightcolor="darkblue", highlightcolor_upreg = "#038C65", highlightcolor_downreg = "#8C0303", nointerestcolor="grey",threshold_y=NULL, threshold_x=NULL, xlab=NULL, ylab=NULL, ncol=NULL, nrow=NULL, title=NULL,labsize=7,widthlabs=25, invert=T)
options(error=recover)
GSEAresults <- runGSEA(degenes, bidirectsigs, stat = NULL)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some pathways, in reality P-values are less than 1e-50. You can set the `eps` argument to zero for better estimation.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, :
For some pathways, in reality P-values are less than 1e-50. You can set the `eps` argument to zero for better estimation.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
GSEAresults
$`Senescent-Proliferative`
$`Senescent-Quiescent`
$`Proliferative-Quiescent`
NA
plotGSEAenrichment(GSEA_results=GSEAresults, DEGList=degenes, gene_sets=bidirectsigs, widthTitle=32,grid = T, titlesize = 10, nrow=3, ncol=9)
options(error=recover)
plotNESlollipop(GSEA_results=GSEAresults, sig_threshold = 0.05,saturation_value=0.00001, nonsignif_color = "white", signif_color = "red",
grid = T, nrow = 1, ncol = NULL, widthlabels=28, title=NULL)
plotCombinedGSEA(GSEAresults, sig_threshold = 0.05, PointSize=9, widthlegend = 26 )
If the user is investigating if a certain variable can be described from the GSEA results, and not already knowing that variable. More exploratory…
For each variable, define all possible contrasts (e.g. if A,B,C, then consider A-B, A-C, B-C,A-(B+C)/2, etc…)
Use calculateDE without baseline and all possible contrasts
perform GSEA and collect all results in only one table
Plot all results in lollipop plots, y axis with all contrasts
One function to calculate all results; one to do the lollypop plot
options(error=recover)
df_test <- GSEA_VariableAssociation(data=corrcounts_merge,
metadata=metadata_merge_corr,
cols=c("Condition","Is_Senescent","random_cat","random_numeric"),
mode="extensive",
gene_set=list(HernandezSegura=bidirectsigs$HernandezSegura), saturation_value=0.000000001, nonsignif_color = "white", signif_color = "red",
sig_threshold = 0.05, widthlabels=30, labsize=10, titlesize=14)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
df_test$plot
metadata_corr_1 <- CalculateScores(data = corrcounts_merge, metadata = metadata_merge_corr, method = "ssGSEA", gene_sets = list(HernandezSegura=bidirectsigs$HernandezSegura) )
Considering bidirectional gene signature mode for signature HernandezSegura
No id variables; using all as measure variables
No id variables; using all as measure variables
metadata_corr <- metadata_corr_1$`HernandezSegura`
metadata_corr
VariableAssociation(df=metadata_corr, cols=c("Condition","Is_Senescent","random_cat","random_numeric"),
target_var="score", targetvar_lab="Score",
discrete_colors = list(Is_Senescent=c("Senescent"="pink",
"Non Senescent"="orange")),
continuous_color = "#8C6D03",
color_palette = "Set2",
sizeannot=3, ncol=1, nrow=4,
numeric = "pearson",
categorical_bin = "t.test",
categorical_multi = "anova", title=NULL, titlesize=14,
nonsignif_color = "white", signif_color = "red",
saturation_value=NULL,sig_threshold = 0.05, widthlabels=14, pointSize=5, heights=c(0.3,0.2,0.3,0.2))
`geom_smooth()` using formula = 'y ~ x'
options(error=recover)
df_test <- GSEA_VariableAssociation(data=corrcounts_merge,
metadata=metadata_merge_corr,
cols=c("Condition" ),
mode="medium",
gene_set=list(HernandezSegura_UP=SenescenceSignatures$`[UP]_HernandezSegura` ),
saturation_value=0.000000001, nonsignif_color = "white", signif_color = "red",
sig_threshold = 0.05, widthlabels=30, labsize=10, titlesize=14)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
df_test$plot
df_test <- GSEA_VariableAssociation(data=corrcounts_merge,
metadata=metadata_merge_corr,
cols=c("Condition" ),
mode="medium",
gene_set=list( HernandezSegura_Bidirect_UP=subset(bidirectsigs$HernandezSegura, enrichment==1)),
saturation_value=0.000000001, nonsignif_color = "white", signif_color = "red",
sig_threshold = 0.05, widthlabels=30, labsize=10, titlesize=14)
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
df_test$plot
NA
NA